Mass spectrometry is an analytical technique used to analyze the compounds contained within a sample. A sample can be ionized to generate ions that can be subjected to the influences of electric and magnetic fields and separated into various space components dependent on the mass-to-charge ratio within a mass spectrometer. In this manner it is possible to study the products of ionization of a particular sample, and, by using appropriate calibrations, analyze an unknown sample to determine the relative concentrations of its components. In detecting and measuring the ionic component existing in the exit field of a mass spectrometer, ions of a given mass-to-charge (m/z) ratio can be directed upon an ion collector and the intensity of the corresponding ion current can be measured by means of a direct current amplifier. By varying an analyzing electric or magnetic field or moving the collector in the exit field, various types of ions are caused to fall upon the collector successively and the respective intensities are measured, which can allow the identification and quantification of compounds that correspond to the ions.
A typical process for analyzing the changes in the chemical composition of a food product during processing can include preparing multiple samples (pre-thermal processing) in small vessels (e.g., sealed glass ampoules), which are heated for different times and then rapidly cooled to stop the reactions (Balagiannis et al., 2010, American Chemical Society: Washington D.C. p. 13-25). Each sample is then extracted and concentrated, which can take several days. The composition of each sample is then analyzed using gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) to measure the composition at each time point. Each GC-MS or LC-MS run can take up to 60 minutes to complete and with 3 replicates per sample, can give a maximum throughput of 8 samples per day per instrument. In addition, multiple runs may be required to identify different classes of compounds of interest, e.g., amino acids, free fatty acids and nucleotides, due to the different sample preparation methods used for the different chemical classes. The analysis then produces two types of information, the number of compounds resolved by the chromatography and the number of compounds identified. Typically GC-MS will resolve around 100-200 compounds and identify about 40-50 compounds from the spectral libraries and the available retention indices.
From the data obtained from GC-MS and/or the LC-MS analysis, chemical changes can be plotted to examine the course of the reactions or the kinetics of specific reactions (Balagiannis et al., 2010, American Chemical Society: Washington D.C. p. 13-25). However, such a technique can have limitations. For example, heating individual samples to mimic the exact time/temperature conditions that occur in a real food product can be very difficult. In particular, there can be lags in the time to reach the desired temperature and to cool the vessels to stop the reactions, which can result in the introduction of experimental error in the form of variation and lack of correlation with a real food process.
In addition, analyses with GC-MS and LC-MS provide identification for known compounds, i.e., those compounds whose spectra and chromatographic behavior (retention indices) are known and published. Whereas, identifying unknown compounds detected using GC-MS and LC-MS is a time-consuming task and is not always successful as GC-MS and LC-MS analyses alone provide insufficient data to enable structure elucidation; therefore, the results of such targeted LC- and GC-based analyses are not as “data rich” as they could be. This lack of depth in the analytical data then limits the interpretation that can be achieved using data analysis techniques like principal component analysis (PCA) or partial least squares analysis (PLS), which correlate sample properties with the chemical variables. As data analysis techniques based on GC-MS and LC-MS data consider only a limited number of known chemical entities, it is not possible to fully understand the chemistry occurring during processing of real, complex food systems which contain many previously undescribed entities. Therefore, suggestions about ways to intervene to improve flavor, reduce nutrient destruction or control the formation of undesirable compounds like bitter compounds (Frank et al., Journal of Agricultural and Food Chemistry, 2003. 51(9): p. 2693-2699) or potentially toxic compounds (Stadler, Toxicology Letters, 2014. 229: p. S26-S27) are also limited.
Furthermore, to monitor the chemical reactions involving known chemical entities that occur during food processing using conventional approaches can be challenging unless labeled precursors are added to the food product at the initiation of the food process, e.g., using the CAMOLA technique (Schieberle, Annals of the New York Academy of Sciences, 2005. 1043: p. 236-248). This technique requires the precursors to be synthesized from isotopically enriched starting materials and assumes the added labeled compounds behave exactly the same as the “native” compounds. However, isotopically enriched precursors can react and interact with other compounds in a complex food matrix at a different rate to the non-isotopically labeled compound (the kinetic isotope effect), leading to potentially inaccurate results.
Therefore, there is a need in the art for methods that allow for monitoring, evaluating and identifying chemical reactions that occur during processing of complex food products.